{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T19:56:22Z","timestamp":1778615782673,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Science and Innovation","award":["PID2019-107722RB-C22\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["PID2019-107722RB-C22\/AEI\/10.13039\/501100011033"]}]},{"name":"Spanish Ministry of Science and Innovation","award":["PID2020-117171RA-I00"],"award-info":[{"award-number":["PID2020-117171RA-I00"]}]},{"name":"Spanish Ministry of Science and Innovation","award":["2017SGR1551"],"award-info":[{"award-number":["2017SGR1551"]}]},{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["PID2019-107722RB-C22\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["PID2019-107722RB-C22\/AEI\/10.13039\/501100011033"]}]},{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["PID2020-117171RA-I00"],"award-info":[{"award-number":["PID2020-117171RA-I00"]}]},{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["2017SGR1551"],"award-info":[{"award-number":["2017SGR1551"]}]},{"name":"Government of Catalonia","award":["PID2019-107722RB-C22\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["PID2019-107722RB-C22\/AEI\/10.13039\/501100011033"]}]},{"name":"Government of Catalonia","award":["PID2020-117171RA-I00"],"award-info":[{"award-number":["PID2020-117171RA-I00"]}]},{"name":"Government of Catalonia","award":["2017SGR1551"],"award-info":[{"award-number":["2017SGR1551"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with the aim of improving the overall performance of prediction models based on machine learning techniques. Experiments were performed on two cohorts of patients suffering from type 1 diabetes mellitus with significant differences in their clinical outcomes. In the first contribution, we have demonstrated that the chosen methodology is able to replicate the intrinsic characteristics of individual patients following the statistical distributions of the original data. Next, a second contribution demonstrates the potential of synthetic data to improve the performance of machine learning approaches by testing and comparing different prediction models for the problem of predicting nocturnal hypoglycemic events in type 1 diabetic patients. The results obtained for both generative and predictive models are quite encouraging and set a precedent in the use of generative techniques to train new machine learning models.<\/jats:p>","DOI":"10.3390\/s22134944","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T01:40:36Z","timestamp":1656639636000},"page":"4944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0703-6925","authenticated-orcid":false,"given":"Josep","family":"Noguer","sequence":"first","affiliation":[{"name":"Institut d\u2019Inform\u00e0tica i Aplicacions, Universitat de Girona, 17003 Girona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6896-818X","authenticated-orcid":false,"given":"Ivan","family":"Contreras","sequence":"additional","affiliation":[{"name":"Institut d\u2019Inform\u00e0tica i Aplicacions, Universitat de Girona, 17003 Girona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9694-9621","authenticated-orcid":false,"given":"Omer","family":"Mujahid","sequence":"additional","affiliation":[{"name":"Institut d\u2019Inform\u00e0tica i Aplicacions, Universitat de Girona, 17003 Girona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8207-2259","authenticated-orcid":false,"given":"Aleix","family":"Beneyto","sequence":"additional","affiliation":[{"name":"Institut d\u2019Inform\u00e0tica i Aplicacions, Universitat de Girona, 17003 Girona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6884-9789","authenticated-orcid":false,"given":"Josep","family":"Vehi","sequence":"additional","affiliation":[{"name":"Institut d\u2019Inform\u00e0tica i Aplicacions, Universitat de Girona, 17003 Girona, Spain"},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red de Diabetes y Enfermedades Metab\u00f3licas Asociadas (CIBERDEM), 28029 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102120","DOI":"10.1016\/j.artmed.2021.102120","article-title":"Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction\u2014A systematic literature review","volume":"118","author":"Felizardo","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.cmpb.2016.08.022","article-title":"Profiling intra-patient type I diabetes behaviors","volume":"136","author":"Contreras","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijmedinf.2019.03.008","article-title":"Risk-based postprandial hypoglycemia forecasting using supervised learning","volume":"126","author":"Oviedo","year":"2019","journal-title":"Int. J. Med. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.cmpb.2019.06.025","article-title":"Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques","volume":"178","author":"Oviedo","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_5","first-page":"71","article-title":"The OhioT1DM Dataset For Blood Glucose Level Prediction","volume":"2675","author":"Marling","year":"2018","journal-title":"CEUR Workshop Proc."},{"key":"ref_6","unstructured":"Kahn, M. (2022, May 30). Diabetes. UCI Machine Learning Repository. Available online: https:\/\/archive-beta.ics.uci.edu\/ml\/datasets\/diabetes."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e10775","DOI":"10.2196\/10775","article-title":"Artificial Intelligence for Diabetes Management and Decision Support: Literature Review","volume":"20","author":"Contreras","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Contreras, I., Oviedo, S., Vettoretti, M., Visentin, R., and Veh\u00ed, J. (2017). Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0187754"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.artmed.2019.07.007","article-title":"Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes","volume":"98","author":"Woldaregay","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1177\/1932296813514502","article-title":"The UVA\/PADOVA type 1 diabetes simulator: New features","volume":"8","author":"Micheletto","year":"2014","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alkhalifah, T., Wang, H., and Ovcharenko, O. (2021, January 18\u201321). MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning. Proceedings of the 82nd EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, Amsterdam, The Netherlands.","DOI":"10.3997\/2214-4609.202113262"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105568","DOI":"10.1016\/j.cmpb.2020.105568","article-title":"A GAN-based image synthesis method for skin lesion classification","volume":"195","author":"Qin","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rashid, H., Tanveer, M.A., and Aqeel Khan, H. (2019, January 23\u201327). Skin Lesion Classification Using GAN based Data Augmentation. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857905"},{"key":"ref_14","first-page":"1","article-title":"Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network","volume":"9","author":"Zhu","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Piacentino, E., Guarner, A., and Angulo, C. (2021). Generating Synthetic ECGs Using GANs for Anonymizing Healthcare Data. Electronics, 10.","DOI":"10.3390\/electronics10040389"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.1109\/JBHI.2020.2980262","article-title":"Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN)","volume":"24","author":"Yoon","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-021-00480-x","article-title":"Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients","volume":"4","author":"Deng","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_18","unstructured":"De Paula, F., Black, D.M., and Rossen, J. (2017). Williams. Tratado de Endocrinolog\u00eda, Elsevier. [13th ed.]."},{"key":"ref_19","unstructured":"Jameson, J.L. (2017). Harrison\u2019s Endocrinology, Mc Graw Hill Education. [4th ed.]."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"American Diabetes Association (2020). Glycemic Targets: Standards of Medical Care in Diabetes\u20142020. Diabetes Care, 43, S66\u2013S76.","DOI":"10.2337\/dc20-S006"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bertachi, A., Vi\u00f1als, C., Biagi, L., Contreras, I., Veh\u00ed, J., Conget, I., and Gim\u00e9nez, M. (2020). Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor. Sensors, 20.","DOI":"10.3390\/s20061705"},{"key":"ref_22","first-page":"852","article-title":"Alias-Free Generative Adversarial Networks","volume":"34","author":"Karras","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1080\/14697688.2020.1730426","article-title":"Quant GANs: Deep generation of financial time series","volume":"20","author":"Wiese","year":"2020","journal-title":"Quant. Financ."},{"key":"ref_24","first-page":"2672","article-title":"Generative Adversarial Nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/JBHI.2019.2908488","article-title":"Convolutional Recurrent Neural Networks for Glucose Prediction","volume":"24","author":"Li","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_26","unstructured":"Fuglede, B., and Topsoe, F. (2004, January 24\u201329). Jensen-Shannon divergence and Hilbert space embedding. Proceedings of the International Symposium on Information Theory, Chicago, IL, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"045310","DOI":"10.1103\/PhysRevE.105.045310","article-title":"Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time-series analysis","volume":"105","author":"Zunino","year":"2022","journal-title":"Phys. Rev. E"},{"key":"ref_28","unstructured":"Chollet, F. (2022, May 30). Keras, 2015. GitHub. Available online: https:\/\/keras.io."},{"key":"ref_29","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, S., Davis, A., Dean, J., and Devin, M. (2022, May 30). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: tensorflow.org."},{"key":"ref_30","unstructured":"McKinney, W. (July, January 28). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"ref_32","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mujahid, O., Contreras, I., and Vehi, J. (2021). Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges. Sensors, 21.","DOI":"10.3390\/s21020546"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e22458","DOI":"10.2196\/22458","article-title":"Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis","volume":"6","author":"Kodama","year":"2021","journal-title":"JMIR Diabetes"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4944\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:41:15Z","timestamp":1760139675000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4944"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,30]]},"references-count":35,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22134944"],"URL":"https:\/\/doi.org\/10.3390\/s22134944","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1038\/s41598-025-01413-4","asserted-by":"object"}]},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,30]]}}}